Parkinson's Disease Detection using XGBoost and Machine Learning
Francisco Santos, D.
Show abstract
This article explores the application of machine learning, specifically the XGBoost algorithm, for the early detection of Parkinsons disease. Parkinsons disease is a prevalent neurodegenerative condition that poses diagnostic challenges, particularly in its early stages. To address these challenges, a comprehensive dataset, including vocal frequency measurements, audio analyses, and demographic data, is employed. Data preprocessing techniques, including Min-Max scaling and Synthetic Minority Over-sampling Technique (SMOTE), are applied to prepare the dataset. The XGBoost model is then developed and fine-tuned to achieve an accuracy of approximately 93.33% in detecting Parkinsons disease. The model exhibits high precision, recall, and F1-Score, making it a valuable tool for early disease detection in the healthcare domain. The study highlights the transformative potential of machine learning in improving patient outcomes and healthcare efficiency.
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